no code implementations • NeurIPS 2021 • Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu
Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model.
4 code implementations • 15 Jun 2021 • Chengxuan Ying, Mingqi Yang, Shuxin Zheng, Guolin Ke, Shengjie Luo, Tianle Cai, Chenglin Wu, Yuxin Wang, Yanming Shen, Di He
In this technical report, we present our solution of KDD Cup 2021 OGB Large-Scale Challenge - PCQM4M-LSC Track.
4 code implementations • 9 Jun 2021 • Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu
Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model.
Ranked #1 on Graph Regression on PCQM4M-LSC
no code implementations • 25 Feb 2021 • Chengxuan Ying, Guolin Ke, Di He, Tie-Yan Liu
In each lazy block, the self-attention distribution is only computed once in the first layer and then is reused in all upper layers.